Big Data in Process Control Systems

ABSTRACT

A big data network or system for a process control system or plant includes a big data apparatus including a data storage area configured to store, using a common data schema, multiple types of process data and/or plant data (such as configuration and real-time data) that is used in, generated by or received by the process control system, and one or more data receiver computing devices to receive the data from multiple nodes or devices. The data may be cached and time-stamped at the nodes and streamed to the big data apparatus for storage. The process control system big data system provides services and/or data analyses to automatically or manually discover prescriptive and/or predictive knowledge, and to determine, based on the discovered knowledge, changes and/or additions to the process control system and to the set of services and/or analyses to optimize the process control system or plant.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/784,041, filed Mar. 4, 2013, entitled “Big Data in Process ControlSystems,” the entire disclosure of which is hereby expresslyincorporated by reference herein for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to process plants and toprocess control systems, and more particularly, to the use of big datain process plants and in process control system.

BACKGROUND

Distributed process control systems, like those used in chemical,petroleum or other process plants, typically include one or more processcontrollers communicatively coupled to one or more field devices viaanalog, digital or combined analog/digital buses, or via a wirelesscommunication link or network. The field devices, which may be, forexample, valves, valve positioners, switches and transmitters (e.g.,temperature, pressure, level and flow rate sensors), are located withinthe process environment and generally perform physical or processcontrol functions such as opening or closing valves, measuring processparameters, etc. to control one or more process executing within theprocess plant or system. Smart field devices, such as the field devicesconforming to the well-known Fieldbus protocol may also perform controlcalculations, alarming functions, and other control functions commonlyimplemented within the controller. The process controllers, which arealso typically located within the plant environment, receive signalsindicative of process measurements made by the field devices and/orother information pertaining to the field devices and execute acontroller application that runs, for example, different control moduleswhich make process control decisions, generate control signals based onthe received information and coordinate with the control modules orblocks being performed in the field devices, such as HART®,WirelessHART®, and FOUNDATION® Fieldbus field devices. The controlmodules in the controller send the control signals over thecommunication lines or links to the field devices to thereby control theoperation of at least a portion of the process plant or system.

Information from the field devices and the controller is usually madeavailable over a data highway to one or more other hardware devices,such as operator workstations, personal computers or computing devices,data historians, report generators, centralized databases, or othercentralized administrative computing devices that are typically placedin control rooms or other locations away from the harsher plantenvironment. Each of these hardware devices typically is centralizedacross the process plant or across a portion of the process plant. Thesehardware devices run applications that may, for example, enable anoperator to perform functions with respect to controlling a processand/or operating the process plant, such as changing settings of theprocess control routine, modifying the operation of the control moduleswithin the controllers or the field devices, viewing the current stateof the process, viewing alarms generated by field devices andcontrollers, simulating the operation of the process for the purpose oftraining personnel or testing the process control software, keeping andupdating a configuration database, etc. The data highway utilized by thehardware devices, controllers and field devices may include a wiredcommunication path, a wireless communication path, or a combination ofwired and wireless communication paths.

As an example, the DeltaV™ control system, sold by Emerson ProcessManagement, includes multiple applications stored within and executed bydifferent devices located at diverse places within a process plant. Aconfiguration application, which resides in one or more workstations orcomputing devices, enables users to create or change process controlmodules and download these process control modules via a data highway todedicated distributed controllers. Typically, these control modules aremade up of communicatively interconnected function blocks, which areobjects in an object oriented programming protocol that performfunctions within the control scheme based on inputs thereto and thatprovide outputs to other function blocks within the control scheme. Theconfiguration application may also allow a configuration designer tocreate or change operator interfaces which are used by a viewingapplication to display data to an operator and to enable the operator tochange settings, such as set points, within the process controlroutines. Each dedicated controller and, in some cases, one or morefield devices, stores and executes a respective controller applicationthat runs the control modules assigned and downloaded thereto toimplement actual process control functionality. The viewingapplications, which may be executed on one or more operator workstations(or on one or more remote computing devices in communicative connectionwith the operator workstations and the data highway), receive data fromthe controller application via the data highway and display this data toprocess control system designers, operators, or users using the userinterfaces, and may provide any of a number of different views, such asan operator's view, an engineer's view, a technician's view, etc. A datahistorian application is typically stored in and executed by a datahistorian device that collects and stores some or all of the dataprovided across the data highway while a configuration databaseapplication may run in a still further computer attached to the datahighway to store the current process control routine configuration anddata associated therewith. Alternatively, the configuration database maybe located in the same workstation as the configuration application.

The architecture of currently known process control plants and processcontrol systems is strongly influenced by limited controller and devicememory, communications bandwidth and controller and device processorcapability. For example, in currently known process control systemarchitectures, the use of dynamic and static non-volatile memory in thecontroller is usually minimized or, at the least, managed carefully. Asa result, during system configuration (e.g., a priori), a user typicallymust choose which data in the controller is to be archived or saved, thefrequency at which it will be saved, and whether or not compression isused, and the controller is accordingly configured with this limited setof data rules. Consequently, data which could be useful introubleshooting and process analysis is often not archived, and if it iscollected, the useful information may have been lost due to datacompression.

Additionally, to minimize controller memory usage in currently knownprocess control systems, selected data that is to be archived or saved(as indicated by the configuration of the controller) is reported to theworkstation or computing device for storage at an appropriate datahistorian or data silo. The current techniques used to report the datapoorly utilizes communication resources and induces excessive controllerloading. Additionally, due to the time delays in communication andsampling at the historian or silo, the data collection and time stampingis often out of sync with the actual process.

Similarly, in batch process control systems, to minimize controllermemory usage, batch recipes and snapshots of controller configurationtypically remain stored at a centralized administrative computing deviceor location (e.g., at a data silo or historian), and are onlytransferred to a controller when needed. Such a strategy introducessignificant burst loads in the controller and in communications betweenthe workstation or centralized administrative computing device and thecontroller.

Furthermore, the capability and performance limitations of relationaldatabases of currently known process control systems, combined with theprevious high cost of disk storage, play a large part in structuringdata into independent entities or silos to meet the objectives ofspecific applications. For example, within the DeltaV™ system, thearchiving of process models, continuous historical data, and batch andevent data are saved in three different application databases or silosof data. Each silo has a different interface to access the data storedtherein.

Structuring data in this manner creates a barrier in the way thathistorized data is accessed and used. For example, the root cause ofvariations in product quality may be associated with data in more thanof these data silos. However, because of the different file structuresof the silos, it is not possible to provide tools that allow this datato be quickly and easily accessed for analysis. Further, audit orsynchronizing functions must be performed to ensure that data acrossdifferent silos is consistent.

The limitations of currently known process plants and process controlsystem discussed above and other limitations may undesirably manifestthemselves in the operation and optimization of process plants orprocess control systems, for instance, during plant operations, troubleshooting, and/or predictive modeling. For example, such limitationsforce cumbersome and lengthy work flows that must be performed in orderto obtain data for troubleshooting and generating updated models.Additionally, the obtained data may be inaccurate due to datacompression, insufficient bandwidth, or shifted time stamps.

“Big data” generally refers to a collection of one or more data setsthat are so large or complex that traditional database management toolsand/or data processing applications (e.g., relational databases anddesktop statistic packages) are not able to manage the data sets withina tolerable amount of time. Typically, applications that use big dataare transactional and end-user directed or focused. For example, websearch engines, social media applications, marketing applications andretail applications may use and manipulate big data. Big data may besupported by a distributed database which allows the parallel processingcapability of modern multi-process, multi-core servers to be fullyutilized.

SUMMARY

A process control system big data network or system for a processcontrol system or plant provides an infrastructure for supporting largescale data mining and data analytics of process data. In an embodiment,the process control big data network or system includes a plurality ofnodes to collect and store all (or almost all) data that is generated,received, and/or observed by devices included in and associated with theprocess control system or plant. In particular, one of the nodes of theprocess control big data network may be a process control system bigdata apparatus. The process control system big data apparatus mayinclude a unitary, logical data storage area configured to store, usinga common format, multiple types of data that are generated by or relatedto the process control system, the process plant, and to one or moreprocesses being controlled by the process plant. For example, theunitary, logical data storage area may store configuration data,continuous data, event data, plant data, data indicative of a useraction, network management data, and data provided by or to systemsexternal to the process control system or plant.

Unlike prior art process control systems, the identity of data that isto be collected at the nodes of the process control system big datanetwork need not be defined or configured into the nodes a priori.Further, the rate at which data is collected at and transmitted from thenodes also need not be configured, selected, or defined a priori.Instead, the process control big data system may automatically collectall data that is generated at, received by or obtained by the nodes atthe rate at which the data is generated, received or obtained, and maycause the collected data to be delivered in high fidelity (e.g., withoutusing lossy data compression or any other techniques that may cause lossof original information) to the process control system big dataapparatus to be stored (and, optionally, delivered to other nodes of thenetwork).

The process control system big data system also may be able to providesophisticated data and trending analyses for any portion of the storeddata. For example, the process control big data system may be able toprovide automatic data analysis across process data (that, in prior artprocess control systems, is contained in different database silos)without requiring any a priori configuration and without requiring anytranslation or conversion. Based on the analyses, the process controlsystem big data system may be able to automatically provide in-depthknowledge discovery, and may suggest changes to or additional entitiesfor the process control system. Additionally or alternatively, theprocess control system big data system may perform actions (e.g.,prescriptive, predictive, or both) based on the knowledge discovery. Theprocess control system big data system may also enable and assist usersin performing manual knowledge discovery, and in planning, configuring,operating, maintaining, and optimizing the process plant and resourcesassociated therewith.

Knowledge discovery and big data techniques within a process controlplant or environment are inherently different than traditional big datatechniques. Typically, traditional big data applications are singularlytransactional, end-user directed, and do not have strict timerequirements or dependencies. For example, a web retailer collects bigdata pertaining to browsed products, purchased products, and customerprofiles, and uses this collected data to tailor advertising and up-sellsuggestions for individual customers as they navigate the retailer's website. If a particular retail transaction (e.g., a particular data point)is inadvertently omitted from the retailer's big data analysis, theeffect of its omission is negligible, especially when the number ofanalyzed data points is very large. In the worst case, an advertisementor up-sell suggestion may not be as closely tailored to a particularcustomer as could have been if the omitted data point had been includedin the retailer's big data analysis.

In process plant and process control environments, though, the dimensionof time and the presence or omission of particular data points iscritical. For example, if a particular data value is not delivered to arecipient component of the process plant within a certain time interval,a process may become uncontrolled, which may result in a fire,explosion, loss of equipment, and/or loss of human life. Furthermore,multiple and/or complex time-based relationships between differentcomponents, entities, and/or processes operating within the processplant and/or external to the process plant may affect operatingefficiency, product quality, and/or plant safety. The knowledgediscovery provided by the process control system big data techniquesdescribed herein may allow such time-based relationships to bediscovered and utilized, thus enabling a more efficient and safe processplant that may produce a higher quality product.

For example, the techniques described herein may automatically discoverthat a combination of a particular input material characteristic, anambient air pressure at a particular line, and a particular unplannedevent may result in an X % degradation of product quality. Thetechniques may also automatically determine that the potential productquality degradation may be mitigated by adjusting a parameter of adifferent process that executes thirty minutes after the unplannedevent, and may automatically take steps to adjust the parameteraccordingly. Accordingly, the knowledge discovery and process controlsystem big data techniques described herein may enable suchrelationships and actions to be discovered and acted upon within aprocess plant or process control environment, as is described in moredetail below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example big data network for a processplant or process control system;

FIG. 2 is a block diagram illustrating an example arrangement ofprovider nodes included in the process control system big data networkof FIG. 1;

FIG. 3 is a block diagram illustrating an example use of appliance datareceivers to store or historize data at the process control system bigdata appliance of FIG. 1;

FIG. 4 is a block diagram illustrating an example use of appliancerequest servicers to access historized data stored at the processcontrol system big data appliance of FIG. 1;

FIG. 5 is a block diagram of an example embodiment of the processcontrol system big data studio of FIG. 1;

FIG. 6 is a block diagram of an example coupling between a configurationand exploration environment provided by the process control system bigdata studio of FIG. 1 and a runtime environment of the process plant orprocess control system; and

FIG. 7 is a flow diagram of an example method of supporting big data ina process control system or process plant.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of an example big data network 100 for aprocess plant or process control system 10. The example process controlsystem big data network 100 includes a process control system big dataapparatus or appliance 102, a process control system big data networkbackbone 105, and a plurality of nodes 108 that are communicativelyconnected to the backbone 105. Process-related data, plant-related data,and other types of data may be collected and cached at the plurality ofnodes 108, and the data may be delivered, via the network backbone 105,to the process control system big data apparatus or appliance 102 forlong-term storage (e.g., “historization”) and processing. In anembodiment, at least some of the data may be delivered between nodes ofthe network 100, e.g., to control a process in real-time.

Any type of data related to the process control system 10 may becollected and stored at the process control system big data appliance102. In an embodiment, process data may be collected and stored. Forexample, real-time process data such as continuous, batch, measurementand event data that is generated while a process is being controlled inthe process plant 10 (and, in some cases, is indicative of an effect ofa real-time execution of the process) may be collected and stored.Process definition, arrangement or set-up data such as configurationdata and/or batch recipe data may be collected and stored. Datacorresponding to the configuration, execution and results of processdiagnostics may be collected and stored. Other types of process data mayalso be collected and stored.

In an embodiment, data highway traffic and network management data ofthe backbone 105 and of various other communication networks of theprocess plant 10 may be collected and stored. In an embodiment,user-related data such as data related to user traffic, login attempts,queries and instructions may be collected and stored. Text data (e.g.,logs, operating procedures, manuals, etc.), spatial data (e.g.,location-based data) and multi-media data (e.g., closed circuit TV,video clips, etc.) may be collected and stored.

In an embodiment, data that is related to the process plant 10 (e.g., tophysical equipment included in the process plant 10 such as machines anddevices) but that may not be generated by applications that directlyconfigure, control, or diagnose a process may be collected and stored.For example, vibration data and steam trap data may be collected andstored. Plant safety data may be collected and stored. For example, dataindicative of a value of a parameter corresponding to plant safety(e.g., corrosion data, gas detection data, etc.) may be stored, or dataindicative of an event corresponding to plant safety may be stored. Datacorresponding to the health of machines, plant equipment and/or devicesmay be collected and stored. For example, equipment data (e.g., pumphealth data determined based on vibration data and other data) may becollected. Data corresponding to the configuration, execution andresults of equipment, machine, and/or device diagnostics may becollected and stored.

In some embodiments, data generated by or transmitted to entitiesexternal to the process plant 10 may be collected and stored, such asdata related to costs of raw materials, expected arrival times of partsor equipment, weather data, and other external data. In an embodiment,all data that is generated, received, or observed by all nodes 108 thatare communicatively connected to the network backbone 105 may becollected and caused to be stored at the process control system big dataappliance 102.

In an embodiment, the process control system big data network 100includes a process control system big data studio 109 configured toprovide a primary interface into the process control system big datanetwork 100 for configuration and data exploration, e.g., a userinterface or an interface for use by other applications. The processcontrol system big data studio 109 may be connected to the big dataappliance 102 via the process control system big data network backbone105, or may be directly connected to the process control system big dataappliance 102.

Process Control Big Data Network Nodes

The plurality of nodes 108 of the process control big data network 100may include several different groups of nodes 110-115. A first group ofnodes 110, referred to herein as “provider nodes 110” or “providerdevices 110,” may include one or more nodes or devices that generate,route, and/or receive process control data to enable processes to becontrolled in real-time in the process plant environment 10. Examples ofprovider devices or nodes 110 may include devices whose primary functionis directed to generating and/or operating on process control data tocontrol a process, e.g., wired and wireless field devices, controllers,or input/output (I/O devices). Other examples of provider devices 110may include devices whose primary function is to provide access to orroutes through one or more communication networks of the process controlsystem (of which the process control big network 100 is one), e.g.,access points, routers, interfaces to wired control busses, gateways towireless communication networks, gateways to external networks orsystems, and other such routing and networking devices. Still otherexamples of provider devices 110 may include devices whose primaryfunction is to temporarily store process data and other related datathat is accumulated throughout the process control system 10 and tocause the temporarily stored data to be transmitted for historization atthe process control system big data appliance 102.

In an embodiment, at least one of the provider devices 110 iscommunicatively connected to the process control big data networkbackbone 105 in a direct manner. In an embodiment, at least one of theprovider devices 110 is communicatively connected to the backbone 105 inan indirect manner. For example, a wireless field device may becommunicatively connected to the backbone 105 via a router, and accesspoint, and a wireless gateway. Typically, provider devices 110 do nothave an integral user interface, although some of the provider devices100 may have the capability to be in communicative connection with auser computing device or user interface, e.g., by communicating over awired or wireless communication link, or by plugging a user interfacedevice into a port of the provider device 110.

A second group of nodes 112, referred to herein as “user interface nodes112” or user interface devices 112,” may include one or more nodes ordevices that each have an integral user interface via which a user oroperator may interact with the process control system or process plant10 to perform activities related to the process plant 10 (e.g.,configure, view, monitor, test, analyze, diagnose, order, plan,schedule, annotate, and/or other activities). Examples of these userinterface nodes or devices 112 may include mobile or stationarycomputing devices, workstations, handheld devices, tablets, surfacecomputing devices, and any other computing device having a processor, amemory, and an integral user interface. Integrated user interfaces mayinclude a screen, a keyboard, keypad, mouse, buttons, touch screen,touch pad, biometric interface, speakers and microphones, cameras,and/or any other user interface technology. Each user interface node 112may include one or more integrated user interfaces. User interface nodes112 may include a direct connection to the process control big datanetwork backbone 105, or may include in indirect connection to thebackbone 105, e.g., via an access point or a gateway. User interfacenodes 112 may communicatively connect to the process control system bigdata network backbone 105 in a wired manner and/or in a wireless manner.In some embodiments, a user interface node 112 may connect to thenetwork backbone 105 in an ad-hoc manner.

Of course, the plurality of nodes 108 of the process control big datanetwork 100 is not limited to only provider nodes 110 and user interfacenodes 112. One or more other types of nodes 115 may also be included inthe plurality of nodes 108. For example, a node of a system that isexternal to the process plant 10 (e.g., a lab system or a materialshandling system) may be communicatively connected to the networkbackbone 105 of the system 100. A node or device 115 may becommunicatively connected to the backbone 105 via a direct or anindirect connection. A node or device 115 may be communicativelyconnected to the backbone 105 via a wired or a wireless connection. Insome embodiments, the group of other nodes 115 may be omitted from theprocess control system big data network 100.

In an embodiment, at least some of the nodes 108 of the process controlsystem big data network 100 may include an integrated firewall. Further,any number of the nodes 108 (e.g., zero nodes, one node, or more thanone node) may each include respective memory storage (denoted in FIG. 1by the icons M_(X)) to store or cache tasks, measurements, events, andother data in real-time. In an embodiment, a memory storage M_(X) maycomprise high density memory storage technology, for example, solidstate drive memory, semiconductor memory, optical memory, molecularmemory, biological memory, or any other suitable high density memorytechnology. In some embodiments, the memory storage M_(X) may alsoinclude flash memory. The memory storage M_(X) (and, in some cases, theflash memory) may be configured to temporarily store or cache data thatis generated by, received at, or otherwise observed by its respectivenode 108. The flash memory M_(X) of at least some of the nodes 108(e.g., a controller device) may also store snapshots of nodeconfiguration, batch recipes, and/or other data to minimize delay inusing this information during normal operation or after a power outageor other event that causes the node to be off-line. In an embodiment ofthe process control system big data network 100, all of the nodes 110,112 and any number of the nodes 115 may include high density memorystorage M_(X). It is understood that different types or technologies ofhigh density memory storage M_(X) may be utilized across the set ofnodes 108, or across a subset of nodes included in the set of nodes 108.

In an embodiment, any number of the nodes 108 (for example, zero nodes,one node, or more than one node) may each include respective multi-corehardware (e.g., a multi-core processor or another type of parallelprocessor), as denoted in the FIG. 1 by the icons P_(MCX). At least someof the nodes 108 may designate one of the cores of its respectiveprocessor P_(MCX) for caching real-time data at the node and, in someembodiments, for causing the cached data to be transmitted for storageat the process control system big data appliance 102. Additionally oralternatively, at least some of the nodes 108 may designate more thanone of the multiple cores of its respective multi-core processor P_(MCX)for caching real-time data. In some embodiments, the one or moredesignated cores for caching real-time data (and, in some cases, forcausing the cached data to be stored at big data appliance 102) may beexclusively designated as such (e.g., the one or more designated coresmay perform no other processing except processing related to caching andtransmitting big data). In an embodiment, at least some of the nodes 108may designate one of its cores to perform operations to control aprocess in the process plant 10. In an embodiment, one or more cores maybe designated exclusively for performing operations to control aprocess, and may not be used to cache and transmit big data. It isunderstood that different types or technologies of multi-core processorsP_(MCX) may be utilized across the set of nodes 108, or across a subsetof nodes of the set of nodes 108. In an embodiment of the processcontrol system big data network 100, all of the nodes 110, 112 and anynumber of the nodes 115 may include some type of multi-core processorP_(MCX).

It is noted, though, that while FIG. 1 illustrates the nodes 108 as eachincluding both a multi-core processor P_(MCX) and a high density memoryM_(X), each of the nodes 108 is not required to include both amulti-core processor P_(MCX) and a high density memory M_(X). Forexample, some of the nodes 108 may include only a multi-core processorP_(MCX) and not a high density memory M_(X), some of the nodes 108 mayinclude only a high density memory M_(X) and not a multi-core processorP_(MCX), some of the nodes 108 may include both a multi-core processorP_(MCX) and a high density memory M_(X), and/or some of the nodes 108may include neither a multi-core processor P_(MCX) nor a high densitymemory M_(X).

Examples of real-time data that may be cached or collected by providernodes or devices 110 may include measurement data, configuration data,batch data, event data, and/or continuous data. For instance, real-timedata corresponding to configurations, batch recipes, setpoints, outputs,rates, control actions, diagnostics, alarms, events and/or changesthereto may be collected. Other examples of real-time data may includeprocess models, statistics, status data, and network and plantmanagement data.

Examples of real-time data that may be cached or collected by userinterface nodes or devices 112 may include, for example, user logins,user queries, data captured by a user (e.g., by camera, audio, or videorecording device), user commands, creation, modification or deletion offiles, a physical or spatial location of a user interface node ordevice, results of a diagnostic or test performed by the user interfacedevice 112, and other actions or activities initiated by or related to auser interacting with a user interface node 112.

Collected data may be dynamic or static data. Collected data mayinclude, for example, database data, streaming data, and/ortransactional data. Generally, any data that a node 108 generates,receives, or observes may be collected or cached with a correspondingtime stamp or indication of a time of collection/caching. In a preferredembodiment, all data that a node 108 generates, receives, or observes iscollected or cached in its memory storage (e.g., high density memorystorage M_(X)) with a respective indication of a time of each datum'scollection/caching (e.g., a time stamp).

In an embodiment, each of the nodes 110, 112 (and, optionally, at leastone of the other nodes 115) may be configured to automatically collector cache real-time data and to cause the collected/cached data to bedelivered to the big data appliance 102 and/or to other nodes 108without requiring lossy data compression, data sub-sampling, orconfiguring the node for data collection purposes. Unlike prior artprocess control systems, the identity of data that is collected at thenodes or devices 108 of the process control system big data network 100need not be configured into the devices 108 a priori. Further, the rateat which data is collected at and delivered from the nodes 108 also neednot be configured, selected or defined. Instead, the nodes 110, 112(and, optionally, at least one of the other nodes 115) of the processcontrol big data system 100 may automatically collect all data that isgenerated by, received at, or obtained by the node at the rate at whichthe data is generated, received or obtained, and may cause the collecteddata to be delivered in high fidelity (e.g., without using lossy datacompression or any other techniques that may cause loss of originalinformation) to the process control big data appliance 102 and,optionally, to other nodes 108 of the network 100.

A detailed block diagram illustrating example provider nodes 110connected to process control big data network backbone 105 is shown inFIG. 2. As previously discussed, provider nodes 110 may include deviceswhose main function is to automatically generate and/or receive processcontrol data that is used to perform functions to control a process inreal-time in the process plant environment 10, such as processcontrollers, field devices and I/O devices. In a process plantenvironment 10, process controllers receive signals indicative ofprocess measurements made by field devices, process this information toimplement a control routine, and generate control signals that are sentover wired or wireless communication links to other field devices tocontrol the operation of a process in the plant 10. Typically, at leastone field device performs a physical function (e.g., opening or closinga valve, increase or decrease a temperature, etc.) to control theoperation of a process, and some types of field devices may communicatewith controllers using I/O devices. Process controllers, field devices,and I/O devices may be wired or wireless, and any number and combinationof wired and wireless process controllers, field devices and I/O devicesmay be nodes 110 of the process control big data network 100.

FIG. 2 illustrates a controller 11 that is communicatively connected towired field devices 15-22 via input/output (I/O) cards 26 and 28, andthat is communicatively connected to wireless field devices 40-46 via awireless gateway 35 and the network backbone 105. (In anotherembodiment, though, the controller 11 may be communicatively connectedto the wireless gateway 35 using a communications network other than thebackbone 105, such as by using another wired or a wireless communicationlink.) In FIG. 2, the controller 11 is shown as being a node 110 of theprocess control system big data network 100, and is directly connectedto the process control big data network backbone 105.

The controller 11, which may be, by way of example, the DeltaV™controller sold by Emerson Process Management, may operate to implementa batch process or a continuous process using at least some of the fielddevices 15-22 and 40-46. The controller 11 may be communicativelyconnected to the field devices 15-22 and 40-46 using any desiredhardware and software associated with, for example, standard 4-20 madevices, I/O cards 26, 28, and/or any smart communication protocol suchas the FOUNDATION® Fieldbus protocol, the HART® protocol, theWirelessHART® protocol, etc. In an embodiment, the controller 11 may beadditionally or alternatively communicatively connected with at leastsome of the field devices 15-22 and 40-46 using the big data networkbackbone 105. In the embodiment illustrated in FIG. 2, the controller11, the field devices 15-22 and the I/O cards 26, 28 are wired devices,and the field devices 40-46 are wireless field devices. Of course, thewired field devices 15-22 and wireless field devices 40-46 could conformto any other desired standard(s) or protocols, such as any wired orwireless protocols, including any standards or protocols developed inthe future.

The controller 11 of FIG. 2 includes a processor 30 that implements oroversees one or more process control routines (stored in a memory 32),which may include control loops. The processor 30 may communicate withthe field devices 15-22 and 40-46 and with other nodes (e.g., nodes 110,112, 115) that are communicatively connected to the backbone 105. Itshould be noted that any control routines or modules (including qualityprediction and fault detection modules or function blocks) describedherein may have parts thereof implemented or executed by differentcontrollers or other devices if so desired. Likewise, the controlroutines or modules described herein which are to be implemented withinthe process control system 10 may take any form, including software,firmware, hardware, etc. Control routines may be implemented in anydesired software format, such as using object oriented programming,ladder logic, sequential function charts, function block diagrams, orusing any other software programming language or design paradigm. Thecontrol routines may be stored in any desired type of memory, such asrandom access memory (RAM), or read only memory (ROM). Likewise, thecontrol routines may be hard-coded into, for example, one or moreEPROMs, EEPROMs, application specific integrated circuits (ASICs), orany other hardware or firmware elements. Thus, the controller 11 may beconfigured to implement a control strategy or control routine in anydesired manner.

In some embodiments, the controller 11 implements a control strategyusing what are commonly referred to as function blocks, wherein eachfunction block is an object or other part (e.g., a subroutine) of anoverall control routine and operates in conjunction with other functionblocks (via communications called links) to implement process controlloops within the process control system 10. Control based functionblocks typically perform one of an input function, such as thatassociated with a transmitter, a sensor or other process parametermeasurement device, a control function, such as that associated with acontrol routine that performs PID, fuzzy logic, etc. control, or anoutput function which controls the operation of some device, such as avalve, to perform some physical function within the process controlsystem 10. Of course, hybrid and other types of function blocks exist.Function blocks may be stored in and executed by the controller 11,which is typically the case when these function blocks are used for, orare associated with standard 4-20 ma devices and some types of smartfield devices such as HART devices, or may be stored in and implementedby the field devices themselves, which can be the case with Fieldbusdevices. The controller 11 may include one or more control routines 38that may implement one or more control loops. Each control loop istypically referred to as a control module, and may be performed byexecuting one or more of the function blocks.

The wired field devices 15-22 may be any types of devices, such assensors, valves, transmitters, positioners, etc., while the I/O cards 26and 28 may be any types of I/O devices conforming to any desiredcommunication or controller protocol. In the embodiment illustrated inFIG. 2, the field devices 15-18 are standard 4-20 ma devices or HARTdevices that communicate over analog lines or combined analog anddigital lines to the I/O card 26, while the field devices 19-22 aresmart devices, such as FOUNDATION® Fieldbus field devices, thatcommunicate over a digital bus to the I/O card 28 using a Fieldbuscommunications protocol. In some embodiments, though, at least some ofthe wired field devices 15-22 and/or at least some of the I/O cards 26,28 may communicate with the controller 11 using the big data networkbackbone 105. In some embodiments, at least some of the wired fielddevices 15-22 and/or at least some of the I/O cards 26, 28 may be nodesof the process control system big data network 100.

In the embodiment shown in FIG. 2, the wireless field devices 40-46communicate in a wireless network 70 using a wireless protocol, such asthe WirelessHART protocol. Such wireless field devices 40-46 maydirectly communicate with one or more other nodes 108 of the processcontrol big data network 100 that are also configured to communicatewirelessly (using the wireless protocol, for example). To communicatewith one or more other nodes 108 that are not configured to communicatewirelessly, the wireless field devices 40-46 may utilize a wirelessgateway 35 connected to the backbone 105 or to another process controlcommunication network. In some embodiments, at least some of thewireless field devices 40-46 may be nodes of the process control systembig data network 100.

The wireless gateway 35 is an example of a provider device 110 that mayprovide access to various wireless devices 40-58 of a wirelesscommunication network 70. In particular, the wireless gateway 35provides communicative coupling between the wireless devices 40-58, thewired devices 11-28, and/or other nodes 108 of the process control bigdata network 100 (including the controller 11 of FIG. 2). For example,the wireless gateway 35 may provide communicative coupling by using thebig data network backbone 105 and/or by using one or more othercommunications networks of the process plant 10.

The wireless gateway 35 provides communicative coupling, in some cases,by the routing, buffering, and timing services to lower layers of thewired and wireless protocol stacks (e.g., address conversion, routing,packet segmentation, prioritization, etc.) while tunneling a sharedlayer or layers of the wired and wireless protocol stacks. In othercases, the wireless gateway 35 may translate commands between wired andwireless protocols that do not share any protocol layers. In addition toprotocol and command conversion, the wireless gateway 35 may providesynchronized clocking used by time slots and superframes (sets ofcommunication time slots spaced equally in time) of a scheduling schemeassociated with the wireless protocol implemented in the wirelessnetwork 70. Furthermore, the wireless gateway 35 may provide networkmanagement and administrative functions for the wireless network 70,such as resource management, performance adjustments, network faultmitigation, monitoring traffic, security, and the like. The wirelessgateway 35 may be a node 110 of the process control system big datanetwork 100.

Similar to the wired field devices 15-22, the wireless field devices40-46 of the wireless network 70 may perform physical control functionswithin the process plant 10, e.g., opening or closing valves or takemeasurements of process parameters. The wireless field devices 40-46,however, are configured to communicate using the wireless protocol ofthe network 70. As such, the wireless field devices 40-46, the wirelessgateway 35, and other wireless nodes 52-58 of the wireless network 70are producers and consumers of wireless communication packets.

In some scenarios, the wireless network 70 may include non-wirelessdevices. For example, a field device 48 of FIG. 2 may be a legacy 4-20mA device and a field device 50 may be a traditional wired HART device.To communicate within the network 70, the field devices 48 and 50 may beconnected to the wireless communication network 70 via a wirelessadaptor (WA) 52 a or 52 b. Additionally, the wireless adaptors 52 a, 52b may support other communication protocols such as Foundation®Fieldbus, PROFIBUS, DeviceNet, etc. Furthermore, the wireless network 70may include one or more network access points 55 a, 55 b, which may beseparate physical devices in wired communication with the wirelessgateway 35 or may be provided with the wireless gateway 35 as anintegral device. The wireless network 70 may also include one or morerouters 58 to forward packets from one wireless device to anotherwireless device within the wireless communication network 70. Thewireless devices 32-46 and 52-58 may communicate with each other andwith the wireless gateway 35 over wireless links 60 of the wirelesscommunication network 70.

Accordingly, FIG. 2 includes several examples of provider devices 110which primarily serve to provide network routing functionality andadministration to various networks of the process control system. Forexample, the wireless gateway 35, the access points 55 a, 55 b, and therouter 58 include functionality to route wireless packets in thewireless communication network 70. The wireless gateway 35 performstraffic management and administrative functions for the wireless network70, as well as routes traffic to and from wired networks that are incommunicative connection with the wireless network 70. The wirelessnetwork 70 may utilize a wireless process control protocol thatspecifically supports process control messages and functions, such asWirelessHART.

The provider nodes 110 of the process control big data network 100,though, may also include other nodes that communicate using otherwireless protocols. For example, the provider nodes 110 may include oneor more wireless access points 72 that utilize other wireless protocols,such as WiFi or other IEEE 802.11 compliant wireless local area networkprotocols, mobile communication protocols such as WiMAX (WorldwideInteroperability for Microwave Access), LTE (Long Term Evolution) orother ITU-R (International Telecommunication Union RadiocommunicationSector) compatible protocols, short-wavelength radio communications suchas near field communications (NFC) and Bluetooth, or other wirelesscommunication protocols. Typically, such wireless access points 72 allowhandheld or other portable computing devices (e.g., user interfacedevices 112) to communicative over a respective wireless network that isdifferent from the wireless network 70 and that supports a differentwireless protocol than the wireless network 70. In some scenarios, inaddition to portable computing devices, one or more process controldevices (e.g., controller 11, field devices 15-22, or wireless devices35, 40-58) may also communicate using the wireless supported by theaccess points 72.

Additionally or alternatively, the provider nodes 110 may include one ormore gateways 75, 78 to systems that are external to the immediateprocess control system 10. Typically, such systems are customers orsuppliers of information generated or operated on by the process controlsystem 10. For example, a plant gateway node 75 may communicativelyconnect the immediate process plant 10 (having its own respectiveprocess control big data network backbone 105) with another processplant having its own respective process control big data networkbackbone. In an embodiment, a single process control big data networkbackbone 105 may service multiple process plants or process controlenvironments.

In another example, a plant gateway node 75 may communicatively connectthe immediate process plant 10 to a legacy or prior art process plantthat does not include a process control big data network 100 or backbone105. In this example, the plant gateway node 75 may convert or translatemessages between a protocol utilized by the process control big databackbone 105 of the plant 10 and a different protocol utilized by thelegacy system (e.g., Ethernet, Profibus, Fieldbus, DeviceNet, etc.).

The provider nodes 110 may include one or more external system gatewaynodes 78 to communicatively connect the process control big data network100 with the network of an external public or private system, such as alaboratory system (e.g., Laboratory Information Management System orLIMS), an operator rounds database, a materials handling system, amaintenance management system, a product inventory control system, aproduction scheduling system, a weather data system, a shipping andhandling system, a packaging system, the Internet, another provider'sprocess control system, or other external systems.

Although FIG. 2 only illustrates a single controller 11 with a finitenumber of field devices 15-22 and 40-46, this is only an illustrativeand non-limiting embodiment. Any number of controllers 11 may beincluded in the provider nodes 110 of the process control big datanetwork 100, and any of the controllers 11 may communicate with anynumber of wired or wireless field devices 15-22, 40-46 to control aprocess in the plan 10. Furthermore, the process plant 10 may alsoinclude any number of wireless gateways 35, routers 58, access points55, wireless process control communication networks 70, access points72, and/or gateways 75, 78.

As previously discussed, one or more of the provider nodes 110 mayinclude a respective multi-core processor P_(MCX), a respective highdensity memory storage M_(X), or both a respective multi-core processorP_(MCX) and a respective high density memory storage M_(X) (denoted inFIG. 2 by the icon BD). Each provider node 100 may utilize its memorystorage M_(X) (and, in some embodiments, its flash memory) to collectand cache data. Each of the nodes 110 may cause its cached data to betransmitted to the process control system big data appliance 102. Forexample, a node 110 may cause at least a portion of the data in itscache to be periodically transmitted to the big data appliance 102.Alternatively or additionally, the node 110 may cause at least a portionof the data in its cached to be streamed to the big data appliance 102.In an embodiment, the process control system big data appliance 102 maybe a subscriber to a streaming service that delivers the cached orcollected data from the node 110. In an embodiment, the provider node110 may host the streaming service.

For nodes 110 that have a direct connection with the backbone 105 (e.g.,the controller 11, the plant gateway 75, the wireless gateway 35), therespective cached or collected data may be transmitted directly from thenode 110 to the process control big data appliance 102 via the backbone105, in an embodiment. For at least some of the nodes 110, though, thecollection and/or caching may be leveled or layered, so that cached orcollected data at a node that is further downstream (e.g., is furtheraway) from the process control big data appliance 102 is intermediatelycached at a node that is further upstream (e.g., is closer to the bigdata appliance 102).

To illustrate layered or leveled data caching, an example scenario isprovided. in this example scenario, referring to FIG. 2, a field device22 caches process control data that it generates or receives, and causesthe contents of its cache to be delivered to an “upstream” deviceincluded in the communication path between the field device 22 and theprocess control big data appliance 102, such as the I/O device 28 or thecontroller 11. For example, the field device 22 may stream the contentsof its cache to the I/O device 28, or the field device 22 mayperiodically transmit the contents of its cache to the I/O device 28.The I/O device 28 caches the information received from the field device22 in its memory M₅ (and, in some embodiments, may also cache datareceived from other downstream field devices 19-21 in its memory M₅)along with other data that the I/O device 28 directly generates,receives and observes. The data that is collected and cached at the I/Odevice 28 (including the contents of the cache of the field device 22)may then be periodically transmitted and/or streamed to the upstreamcontroller 11. Similarly, at the level of the controller 11, thecontroller 11 caches information received from downstream devices (e.g.,the I/O cards 26, 28, and/or any of the field devices 15-22) in itsmemory M₆, and aggregates, in its memory M₆, the downstream data withdata that the controller 11 itself directly generates, receives andobserves. The controller 11 may then periodically deliver and/or streamthe aggregated collected or cached data to the process control big dataappliance 102.

In second example scenario of layered or leveled caching, the controller11 controls a process using wired field devices (e.g., one or more ofthe devices 15-22) and at least one wireless field device (e.g.,wireless field device 44). In a first embodiment of this second examplescenario, the cached or collected data at the wireless device 44 isdelivered and/or streamed directly to the controller 11 from thewireless device 44 (e.g., via the big data network 105), and is storedat the controller cache M₆ along with data from other devices or nodesthat are downstream from the controller 11. The controller 11 mayperiodically deliver or stream the data stored in its cache M₆ to theprocess control big data appliance 102.

In another embodiment of this second example scenario, the cached orcollected data at the wireless device 44 may be ultimately delivered tothe process control big data appliance 102 via an alternate leveled orlayered path, e.g., via the device 42 a, the router 52 a, the accesspoint 55 a, and the wireless gateway 35. In this embodiment, at leastsome of the nodes 41 a, 52 a, 55 a or 35 of the alternate path may cachedata from downstream nodes and may periodically deliver or stream itscached data to a node that is further upstream.

Accordingly, different types of data may be cached at different nodes ofthe process control system big data network 100 using different layeringor leveling arrangements. In an embodiment, data corresponding tocontrolling a process may be cached and delivered in a layered mannerusing provider devices 110 whose primary functionality is control (e.g.,field devices, I/O cards, controllers), whereas data corresponding tonetwork traffic measurement may be cached and delivered in a layeredmanner using provider devices 110 whose primary functionality is trafficmanagement (e.g., routers, access points, and gateways). In anembodiment, data may be delivered via provider nodes or devices 110whose primary function (and, in some scenarios, sole function) is tocollect and cache data from downstream devices (referred to herein as“historian nodes”). For example, a leveled system of historian nodes orcomputing devices may be located throughout the network 100, and eachnode 110 may periodically deliver or stream cached data to a historiannode of a similar level, e.g., using the backbone 105. Downstreamhistorian nodes may deliver or stream cached data to upstream historiannodes, and ultimately the historian nodes that are immediatelydownstream of the process control big data appliance 102 may deliver orstream respective cached data for storage at the process control bigdata appliance 102.

In an embodiment, layered caching may be performed by nodes 110 thatcommunicate with each other using the process control system big datanetwork backbone 105. In an embodiment, at least some of the nodes 110may communicate cached data to other nodes 110 at a different levelusing another communication network and/or other protocol, such as HART,WirelessHART, Fieldbus, DeviceNet, WiFi, Ethernet, or other protocol.

Of course, while leveled or layered caching has been discussed withrespect to provider nodes 110, the concepts and techniques may applyequally to user interface nodes 112 and/or to other types of nodes 115of the process control system big data network 100. In an embodiment, asubset of the nodes 108 may perform leveled or layered caching, whileanother subset of the nodes 108 may cause their cached/collected data tobe directly delivered to the process control big data appliance 102without being cached or temporarily stored at an intermediate node. Insome embodiments, historian nodes may cache data from multiple differenttypes of nodes, e.g., from a provider node 110 and from a user interfacenode 112.

Process Control System Big Data Network Backbone

Returning to FIG. 1, the process control system big data networkbackbone 105 may include a plurality of networked computing devices orswitches that are configured to route packets to/from various nodes 108of the process control system big data network 100 and to/from theprocess control big data appliance 102 (which is itself a node of theprocess control system big data network 100). The plurality of networkedcomputing devices of the backbone 105 may be interconnected by anynumber of wireless and/or wired links. In an embodiment, the processcontrol system big data network backbone 105 may include one or morefirewall devices.

The big data network backbone 105 may support one or more suitablerouting protocols, e.g., protocols included in the Internet Protocol(IP) suite (e.g., UPD (User Datagram Protocol), TCP (TransmissionControl Protocol), Ethernet, etc.), or other suitable routing protocols.In an embodiment, at least some of the nodes 108 utilize a streamingprotocol such as the Stream Control Transmission Protocol (SCTP) tostream cached data from the nodes to the process control big dataappliance 102 via the network backbone 105. Typically, each node 108included in the process data big data network 100 may support at leastan application layer (and, for some nodes, additional layers) of therouting protocol(s) supported by the backbone 105. In an embodiment,each node 108 is uniquely identified within the process control systembig data network 100, e.g., by a unique network address.

In an embodiment, at least a portion of the process control system bigdata network 100 may be an ad-hoc network. As such, at least some of thenodes 108 may connect to the network backbone 105 (or to another node ofthe network 100) in an ad-hoc manner. In an embodiment, each node thatrequests to join the network 100 must be authenticated. Authenticationis discussed in more detail in later sections.

Process Control System Big Data Appliance

Continuing with FIG. 1, in the example process control system big dataprocess control network 100, the process control big data apparatus orappliance 102 is centralized within the network 100, and is configuredto receive data (e.g., via streaming and/or via some other protocol)from the nodes 108 of the network 100 and to store the received data. Assuch, the process control big data apparatus or appliance 102 mayinclude a data storage area 120 for historizing or storing the data thatis received from the nodes 108, a plurality of appliance data receivers122, and a plurality of appliance request servicers 125. Each of thesecomponents 120, 122, 125 of the process control big data appliance 102is described in more detail below.

The process control system big data storage area 120 may comprisemultiple physical data drives or storage entities, such as RAID(Redundant Array of Independent Disks) storage, cloud storage, or anyother suitable data storage technology that is suitable for data bank ordata center storage. However, to the nodes 108 of the network 100, thedata storage area 120 has the appearance of a single or unitary logicaldata storage area or entity. As such, the data storage 120 may be viewedas a centralized big data storage area 120 for the process control bigdata network 100 or for the process plant 10. In some embodiments, asingle logical centralized data storage area 120 may service multipleprocess plants (e.g., the process plant 10 and another process plant).For example, a centralized data storage area 120 may service severalrefineries of an energy company. In an embodiment, the centralized datastorage area 120 may be directly connected to the backbone 105. In someembodiments, the centralized data storage area 120 may be connected tothe backbone 105 via at least one high-bandwidth communication link. Inan embodiment, the centralized data storage area 120 may include anintegral firewall.

The structure of the unitary, logical data storage area 120 supports thestorage of all process control system related data, in an embodiment.For example, each entry, data point, or observation of the data storageentity may include an indication of the identity of the data (e.g.,source, device, tag, location, etc.), a content of the data (e.g.,measurement, value, etc.), and a time stamp indicating a time at whichthe data was collected, generated, received or observed. As such, theseentries, data points, or observations are referred to herein as“time-series data.” The data may be stored in the data storage area 120using a common format including a schema that supports scalable storage,streamed data, and low-latency queries, for example.

In an embodiment, the schema may include storing multiple observationsin each row, and using a row-key with a custom hash to filter the datain the row. The hash is based on the time stamp and a tag, in anembodiment. For example, the hash may be a rounded value of the timestamp, and the tag may correspond to an event or an entity of or relatedto the process control system. In an embodiment, metadata correspondingto each row or to a group of rows may also be stored in the data storagearea 120, either integrally with the time-series data or separately fromthe time-series data. For example, the metadata may be stored in aschema-less manner separately from the time-series data.

In an embodiment, the schema used for storing data at the appliance datastorage 120 is also utilized for storing data in the cache M_(X) of atleast one of the nodes 108. Accordingly, in this embodiment, the schemais maintained when data is transmitted from the local storage areasM_(X) of the nodes 108 across the backbone 105 to the process controlsystem big data appliance data storage 120.

In addition to the data storage 120, the process control system big dataappliance 102 may further include one or more appliance data receivers122, each of which is configured to receive data packets from thebackbone 105, process the data packets to retrieve the substantive dataand timestamp carried therein, and store the substantive data andtimestamp in the data storage area 120. The appliance data receivers 122may reside on a plurality of computing devices or switches, for example.In an embodiment, multiple appliance data receivers 122 (and/or multipleinstances of at least one data receiver 122) may operate in parallel onmultiple data packets.

In embodiments in which the received data packets include the schemautilized by the process control big data appliance data storage area120, the appliance data receivers 122 merely populate additional entriesor observations of the data storage area 120 with the schematicinformation (and, may optionally store corresponding metadata, ifdesired). In embodiments in which the received data packets do notinclude the schema utilized by the process control big data appliancedata storage area 120, the appliance data receivers 122 may decode thepackets and populate time-series data observations or data points of theprocess control big data appliance data storage area 120 (and,optionally corresponding metadata) accordingly.

Additionally, the process control system big data appliance 102 mayinclude one or more appliance request servicers 125, each of which isconfigured to access time-series data and/or metadata stored in theprocess control system big data appliance storage 120, e.g., per therequest of a requesting entity or application. The appliance requestservicers 125 may reside on a plurality of computing devices orswitches, for example. In an embodiment, at least some of the appliancerequest servicers 125 and the appliance data receivers 122 reside on thesame computing device or devices (e.g., on an integral device), or areincluded in an integral application.

In an embodiment, multiple appliance request servicers 125 (and/ormultiple instances of at least one appliance request servicer 125) mayoperate in parallel on multiple requests from multiple requestingentities or applications. In an embodiment, a single appliance requestservicer 125 may service multiple requests, such as multiple requestsfrom a single entity or application, or multiple requests from differentinstances of an application.

FIGS. 3 and 4 are example block diagrams that illustrate more detailedconcepts and techniques which may be achieved using the appliance datareceivers 122 and the appliance request servicers 125 of the processcontrol system big data appliance 102.

FIG. 3 is an example block diagram illustrating the use of the appliancedata receivers 122 to transfer data (e.g., streamed data) from the nodes108 of the process control big data network 100 to the big dataappliance 102 for storage and historization. FIG. 3 illustrates fourexample nodes 108 of FIG. 1, i.e., the controller 11, a user interfacedevice 12, the wireless gateway 35, and a gateway to a third partymachine or network 78. However, the techniques and concepts discussedwith respect to FIG. 3 may be applied to any type and any number of thenodes 108. Additionally, although FIG. 3 illustrates only threeappliance data receivers 122 a, 122 b and 122 c, the techniques andconcepts corresponding to FIG. 3 may be applied to any type and anynumber of appliance data receivers 122.

In the embodiment illustrated in FIG. 3, each of the nodes 11, 12, 35and 78 includes a respective scanner S₁₁, S₁₂, S₃₅, S₇₈ to capture datathat is generated, received or otherwise observed by the node 11, 12, 35and 78. In an embodiment, the functionality of each scanner S₁₁, S₁₂,S₃₅, S₇₈ may be executed by a respective processor P_(MCX) of therespective node 11, 12, 35, 78. The scanner S₁₁, S₁₂, S₃₅, S₇₈ may causethe captured data and a corresponding time stamp to be temporarilystored or cached in a respective local memory M₁₁, M₁₂, M₃₅, M₇₈, forexample, in a manner such as previously described. As such, the captureddata includes time-series data or real-time data. In an embodiment, thecaptured data is stored or cached in each of the memories M₁₁, M₁₂, M₃₅and M₇₈ using the schema utilized by the process control big datastorage area 120.

Each node 11, 12, 35 and 78 may transmit at least some of the cacheddata to one or more appliance data receivers 122 a-122 c, e.g., by usingthe network backbone 105. For example, at least one node 11, 12, 35, 78may push at least some of the data from its respective memory M_(X) whenthe cache is filled to a particular threshold. The threshold of thecache may be adjustable, in an embodiment. In an embodiment, at leastone node 11, 12, 35, 78 may push at least some of data from itsrespective memory M_(X) when a resource (e.g., a bandwidth of thenetwork 105, the processor P_(MCX), or some other resource) issufficiently available. An availability threshold of a particularresource may be adjustable, in an embodiment.

In some embodiments, at least one node 11, 12, 35, 78 may push at leastsome of the data stored in the memories M_(X) at periodic intervals. Theperiodicity of a particular time interval at which data is pushed may bebased on a type of the data, the type of pushing node, the location ofthe pushing node, and/or other criteria. In an embodiment, theperiodicity of a particular time interval may be adjustable. In someembodiments, at least one node 11, 12, 35, 78 may provide data inresponse to a request (e.g., from the process control big data appliance102).

In some embodiments, at least one node 11, 12, 35, 78 may stream atleast some of the data in real-time as the data is generated, receivedor otherwise observed by each node 11, 12, 35, 78 (e.g., the node maynot temporarily store or cache the data, or may store the data for onlyas long as it takes the node to process the data for streaming). Forexample, at least some of the data may be streamed to the one or moreappliance data receivers 122 by using a streaming protocol. In anembodiment, a node 11, 12, 35, 78 may host a streaming service, and atleast one of the data receivers 122 and/or the data storage area 120 maysubscribe to the streaming service.

Accordingly, transmitted data may be received by one or more appliancedata receivers 122 a-122 c, e.g., via the network backbone 105. In anembodiment, a particular appliance data receiver 122 may be designatedto receive data from one or more particular nodes. In an embodiment, aparticular appliance data receiver 122 may be designated to receive datafrom only one or more particular types of devices (e.g., controllers,routers, or user interface devices). In some embodiments, a particularappliance data receiver 122 may be designated to receive only one ormore particular types of data (e.g., network management data only orsecurity-related data only).

The appliance data receivers 122 a-122 c may cause the data to be storedor historized in the big data appliance storage area 120. For example,the data received by each of the appliance data receivers 122 a-122 cmay be stored in the data storage area 120 using the process control bigdata schema. In the embodiment shown in FIG. 3, the time series data 120a is illustrated as being stored separately from corresponding metadata120 b, although in some embodiments, at least some of the metadata 120 bmay be integrally stored with the time series data 120 a.

In an embodiment, data that is received via the plurality of appliancedata receivers 122 a-122 c is integrated so that data from multiplesources may be combined (e.g., into a same group of rows of the datastorage area 120). In an embodiment, data that is received via theplurality of appliance data receivers 122 a-122 c is cleaned to removenoise and inconsistent data. An appliance data receiver 122 may performdata cleaning and/or data integration on at least some of the receiveddata before the received data is stored, in an embodiment, and/or theprocess control system big data appliance 102 may clean some or all ofthe received data after the received data has been stored in the storagearea 102, in an embodiment. In an embodiment, a device or node 110, 112,115 may cause additional data related to the data contents to betransmitted, and the appliance data receiver 122 and/or the big dataappliance storage area 120 may utilize this additional data to performdata cleaning. In an embodiment, at least some data may be cleaned (atleast partially) by a node 110, 112, 115 prior to the node 110, 112, 115causing the data to be transmitted to the big data appliance storagearea 120 for storage.

Turning now to FIG. 4, FIG. 4 is an example block diagram illustratingthe use of appliance request servicers 125 to access the historized datastored at the data storage area 120 of the big data appliance 102. FIG.4 includes a set of appliance request servicers or services 125 a-125 ethat are each configured to access time-series data 120 a and/ormetadata 120 b per the request of a requesting entity or application,such as a data requester 130 a-130 c or a data analysis engine 132 a-132b. While FIG. 4 illustrates five appliance request servicers 125 a-125e, three data requesters 130 a-130 c, and two data analysis engines 132a, 132 b, the techniques and concepts discussed herein with respect toFIG. 4 may be applied to any number and any types of appliance requestservicers 125, data requesters 130, and/or data analysis engines 132.

In an embodiment, at least some of the appliance request servicers 125may each provide a particular service or application that requiresaccess to at least some of the data stored in the process control bigdata storage area 120. For example, the appliance request servicer 125 amay be a data analysis support service, and the appliance requestservicer 125 b may be a data trend support service. Other examples ofservices 125 that may be provided by the process control system big dataappliance 102 may include a configuration application service 125 c, adiagnostic application service 125 d, and an advanced controlapplication service 125 e. An advanced control application service 125 emay include, for example, model predictive control, batch dataanalytics, continuous data analytics or other applications that requirehistorized data for model building and other purposes. Other requestservicers 125 may also be included in the process control system bigdata appliance 102 to support other services or applications, e.g., acommunication service, an administration service, an equipmentmanagement service, a planning service, and other services.

A data requester 130 may be an application that requests access to datathat is stored in the process control system big data appliance storagearea 120. Based on a request of the data requester 130, thecorresponding data may be retrieved from the process control big datastorage area 120, and may be transformed and/or consolidated into dataforms that are usable by the requester 130. In an embodiment, one ormore appliance request servicers 125 may perform data retrieval and/ordata transformation on at least some of the requested data.

At least some of the data requesters 130 and/or at least some of therequest servicers 125 may be web services or web applications that arehosted by the process control system big data appliance 102 and that areaccessible by nodes of the process control system big data network 100(e.g., user interface devices 112 or provider devices 110). Accordingly,at least some of the devices or nodes 108 may include a respective webserver to support a web browser, web client interface, or plug-incorresponding to a data requestor 130 or to a request servicer 125, inan embodiment. For example, a browser or application hosted at a userinterface device 112 may source data or a web page stored at the bigdata appliance 102. For user interface devices 112 in particular, a datarequester 130 or a request servicer 125 may pull displays and storeddata through a User Interface (UI) service layer 135, in an embodiment.

A data analysis engine 132 may be an application that performs acomputational analysis on at least some of the time-series data pointsstored in the appliance storage area 120 to generate knowledge. As such,a data analysis engine 132 may generate a new set of data points orobservations. The new knowledge or new data points may provide aposteriori analysis of aspects of the process plant 10 (e.g.,diagnostics or trouble shooting), and/or may provide a prioripredictions (e.g., prognostics) corresponding to the process plant 10.In an embodiment, a data analysis engine 132 performs data mining on aselected subset of the stored data 120, and performs pattern evaluationon the mined data to generate the new knowledge or new set of datapoints or observations. In some embodiments, multiple data analysisengines 132 or instances thereof may cooperate to generate the newknowledge or new set of data points.

The new knowledge or set of data points may be stored in (e.g., addedto) the appliance storage area 120, for example, and may additionally oralternatively be presented at one or more user interface devices 112. Insome embodiments, the new knowledge may be incorporated into one or morecontrol strategies operating in the process plant 10. A particular dataanalysis engine 132 may be executed when indicated by a user (e.g., viaa user interface device 112), and/or the particular data analysis engine132 may be executed automatically by the process control system big dataappliance 102.

Generally, the data analysis engines 132 of the process control systembig data appliance 102 may operate on the stored data to determinetime-based relationships between various entities and providers withinand external to the process plant 10, and may utilize the determinedtime-based relationship to control one or more processes of the plant 10accordingly. As such, the process control system big data appliance 102allows for one or more processes to be coordinated with other processesand/or to be adjusted over time in response to changing conditions andfactors. In some embodiments, the coordination and/or adjustments may beautomatically determined and executed under the direction of the processcontrol system big data appliance 102 as conditions and events occur,thus greatly increasing efficiencies and optimizing productivity overknown prior art control systems.

Examples of possible scenarios in which the knowledge discoverytechniques of data analysis engines 132 abound. In one example scenario,a certain combination of events leads to poor product quality when theproduct is eventually generated at a later time (e.g., several hoursafter the occurrence of the combination of events). The operator isignorant of the relationship between the occurrence of the events andthe product quality. Rather than detecting and determining the poorproduct quality several hours hence and trouble-shooting to determinethe root causes of the poor product quality (as is currently done inknown process control systems), the process control system big dataappliance 102 (and, in particular, one or more of the data analysisengines 132 therein) may automatically detect the combination of eventsat or shortly after their occurrence, e.g., when the data correspondingto the events' occurrences is transmitted to the appliance 102. The dataanalysis engines 132 may predict the poor product quality based on theoccurrence of these events, may alert an operator to the prediction,and/or may automatically adjust or change one or more parameters orprocesses in real-time to mitigate the effects of the combination ofevents. For example, a data analysis engine 132 may determine a revisedset point or revised parameter values and cause the revised values to beused by provider devices 110 of the process plant 10. In this manner,the process control system big data appliance 102 allows problems to bediscovered and potentially mitigated much more quickly and efficientlyas compared to currently known process control systems.

In another example scenario, at least some of the data analysis engines132 may be utilized to detect changes in product operation. Forinstance, the data analysis engines 132 may detect changes in certaincommunication rates, and/or from changes or patterns of parameter valuesreceived from a sensor or from multiple sensors over time which mayindicate that system dynamics may be changing. In yet another examplescenario, the data analysis engines 132 may be utilized to diagnose anddetermine that a particular batch of valves or other supplier equipmentare faulty based on the behavior of processes and the occurrences ofalarms related to the particular batch across the plant 10 and acrosstime.

In another example scenario, at least some of the data analysis engines132 may predict product capabilities, such as vaccine potency. In yetanother example scenario, the data analysis engines 132 may monitor anddetect potential security issues associated with the process plant 10,such as increases in log-in patterns, retries, and their respectivelocations. In still another example scenario, the data analysis engines132 may analyze data aggregated or stored across the process plant 10and one or more other process plants. In this manner, the processcontrol system big data appliance 102 allows a company that owns oroperates multiple process plants to glean diagnostic and/or prognosticinformation on a region, an industry, or a company-wide basis.

Process Control System Big Data Studio

As previously mentioned with respect to FIG. 1, the process controlsystem big data studio 109 may provide an interface into the exampleprocess control system big data network 100 for configuration and fordata exploration. Accordingly, the process control big data studio 109may be in communicative connection with one or more appliance datareceivers 122 of the process control system big data appliance 102and/or with one or more appliance request servicers 125 of the processcontrol system big data appliance 102. In an embodiment, the processcontrol big data studio 109 may reside on one or more computing devices,zero or more of which may be a computing device on which anothercomponent of the process control big data appliance 102 resides (e.g.,an appliance request servicer 125, an appliance data receiver 122, oranother component). Generally, the process control system big datastudio 109 allows configuration and data exploration to be performed inan off-line environment, and any outputs generated by the studio 109 maybe instantiated into a runtime environment of the process control plant10. As used herein, the term “off-line” indicates that configuration anddata exploration activities are partitioned from the operating plant 10so that configuration and data exploration activities may be performedwithout affecting operations of the process plant 10 even when the plant10 itself is operating or on-line.

A block diagram of an embodiment of the process control system big datastudio 109 is shown in FIG. 5, which is discussed with concurrentreference to FIGS. 1-4. The process control system big data studio 109may provide one or more configuration or exploration applications ortools 145 to enable configuration and data exploration. For example, theapplications or tools 145 may include a dashboard editor 150, a modeleditor 152, a data explorer 155, an analysis editor 158, and/or one ormore other tools or applications 160. Descriptions of each of thesetools 150-160 are provided in later sections.

Each of the tools 150-160 may operate on at least some of the storedtime-series data 120 and/or on one or more definitions 162 that areavailable to the process control system big data studio 109. Thedefinitions 162 may describe building components associated with theprocess control system 10 that may be combined by a tool 145 to generatemore complex components, which may be later instantiated. In anembodiment, the definitions 162 are stored in the process control systembig data storage area 120, or in some other storage location that isaccessible to the big data studio 109.

The definitions 162 that are available to the tools 145 may include, forexample, one or more display component definitions 165 that define ordescribe components that enable various display icons, text, graphicsand views to be presented at a user interface. The display componentdefinitions may include, for example, display element definitions,display view or visualization definitions, binding definitions, etc.

The definitions 162 may include one or more modeling definitions 168.Modeling definitions 168 may define or describe, for example,definitions of products (e.g., products being created by the processplant 10), definitions of equipment or devices (e.g., equipment ordevices included in the process plant 10), definitions of parameters,calculations, function blocks, runtime modules, and other functionalityused to control processes and to otherwise operate, manage or optimizethe process plant 10, and/or other entity definitions. Modelingdefinitions, when instantiated, may be incorporated into a processcontrol model or into other model that is related to the configuration,operation, and/or management of at least a portion of the processcontrol plant 10 and/or processes controlled therein.

The definitions 162 may include one or more data definitions 170, in anembodiment. Data definitions 170 may define a type of data that may beinput into or output a model, such as a process control model, a dataanalysis model, or any other model that is related to the configuration,operation, management, and/or or analysis of at least a portion of theprocess control plant 10 and/or processes controlled therein. Generally,the models into which the defined data is input or output may be createdfrom one or more entities whose definition is included in the modelingdefinitions 168.

As such, data definitions 170 may define or describe various data types(structured and/or unstructured), contexts and/or boundary conditions ofdata that is communicated, generated, received and/or observed withinthe process plant 10. The data definitions 170 may pertain to databasedata, streamed data, transactional data, and/or any other type of datathat is communicated via the process control system big data network 100and is stored or historized in process control system big data storage120. For example, the data stream definitions 170 may describe aparticular data stream as comprising temperatures in degrees Celsiusthat are typically expected to be in the range of Temperature A toTemperature B. The data stream definitions 170 may describe anotherstream data as comprising the connection times and identities of devicesat a particular wireless access point. The data stream definitions 170may describe yet another stream of data as including alarm events at aparticular type of controller. Accordingly, the data stream definitions170 may also include definitions or descriptions of data relationships.For example, the data stream definitions 170 may include a relationshipshowing that alarm event data may be produced by a controller, a sensor,or a device; or the data stream definitions may include a relationshipshowing how a percentage of purity in an input material affects outputquality of a particular line.

In an embodiment, the data definitions 170 may include definitions anddescriptions of data types, contexts, and/or boundary conditions of datathat is utilized by displays, analyses, and other applications relatedto the process plant 10. For example, the data definitions 170 maydescribe Boolean numbers, scientific notation, variable notation, textin different languages, encryption keys, and the like.

Additionally, the definitions 162 may include one or more analyses oralgorithm definitions 172. Analysis definitions 172 may define ordescribe, for example, computational analyses that may be performed on aset of data, e.g., on a selected subset of the stored data 120. Examplesof analyses definitions 172 may include data analyses, (e.g., averages,graphs, histograms, classification techniques, etc.), probabilisticand/or statistical functions (e.g., regression, partial least squares,conditional probabilities, etc.), time-based analysis (e.g., timeseries, Fourier analysis, etc.), visualizations (e.g., bar charts,scatter plots, pie charts, etc.), discovery algorithms, data miningalgorithms, data trending, etc. In an embodiment, at least some of theanalyses definitions 172 may be nested, and/or at least some of theanalyses definitions 172 may be interdependent.

Of course, other definitions 175 in addition to or instead of thedefinitions 165-172 discussed above may be available for use by thetools 145 of the process control system big data studio 109. In anembodiment, at least some of the definitions 162 may be automaticallycreated and stored by the process control system big data appliance 102.In an embodiment, at least some of the definitions 162 may be createdand stored by a user at a user interface 112.

Accordingly, the example process control system big data studio 109includes an interface or portal 180, a respective instance of which maybe presented at each user interface device 112. For example, the processcontrol big studio 109 may host a web service or web applicationcorresponding to the portal 180 that may be accessed at a user interfacedevice 112 via a web browser, plug-in, or web client interface. Inanother example, a user interface of the big data studio 109 may includea client application at a user interface device 112 that communicateswith a host or server application at the process control big data studio109 that corresponds to the portal 180. To a user, the process controlsystem big data studio portal 180 may appear as a navigable display onthe user interface device 112, in an embodiment.

In an embodiment, an access manager 182 of the data studio 109 mayprovide secure access to the data studio 109. A user, a user interfacedevice 112, and/or an access application may be required to beauthenticated by the access manager 182 in order to gain access to thebig data studio 109. In an embodiment, the user may be required toprovide a username and a password or other secure identifier (e.g.,biometric identifier, etc.) to login to the data studio portal 180.Additionally or alternatively, the user, the user interface device 112and/or the access application may be required to be authenticated, suchas by using a Public Key Infrastructure (PKI) encryption algorithm orother algorithm. In an embodiment, a certificate of authentication of aPKI encryption algorithm that is utilized by the user interface device112 may be generated based on at least one parameter such as a spatialor geographical location, a time of access, a context of access, anidentity of the user and/or the user's employer, an identity of theprocess control plant 10, a manufacturer of the user device 112, or someother parameter. In an embodiment, a unique seed corresponding to thecertificate and the shared key may be based on one or more of theparameters.

After authentication, the data studio portal 180 may allow the user, theuser interface device 112, and/or the access application to access thetools or functions 145 of the process control big data studio 109. In anembodiment, an icon corresponding to each tool or function 150-160 maybe displayed at the user interface device 112. Upon selection of aparticular tool 150-160, a series of display views or screens may bepresented to enable the user to utilize the selected tool.

The model editor 152 tool may enable a user to configure (e.g., createor modify) a model for controlling processes in the process controlsystem 10. For example, a user may select and connect various modelingdefinitions 168 (and in some cases, data stream definitions 170) togenerate or change models.

The analysis editor 182 may enable a user to configure (e.g., create ormodify) a data analysis function (e.g., one of the data analysis engines132) for analyzing data related to the process control system 10. Forinstance, a user may configure a complex data analysis function from oneor more analysis definitions 172 (and in some cases, at least some ofthe data stream definitions 170).

A user may explore historized or stored data 120 using the data explorer155. The data explorer 155 may enable a user to view or visualize atleast portions of the stored data 120 based on the data streamdefinitions 170 (and in some cases, based on at least some of theanalysis definitions 172). For example, the data explorer 155 may allowa user to pull temperature data of a particular vat from a particulartime period, and to apply a trending analysis to view the changes intemperature during that time period. In another example, the dataexplorer 155 may allow a user to perform a regression analysis todetermine independent or dependent variables affecting the vattemperature.

In an embodiment, the dashboard editor 150 may enable a user toconfigure dashboard displays or display views. The term “dashboards,” asused herein, generally refers to user interface displays of the runtimeenvironment of the process plant 10 that are displayed on various userinterface devices 112. A dashboard may include a real-time view of anoperation of a portion of a process being controlled in the plant 10, ormay include a view of other data related to the operation of the processplant 10 (e.g., network traffic, technician locations, parts ordering,work order scheduling, etc.), for example. In some embodiments, aruntime dashboard may include a user control to access the data studioportal 180 to enable a user to perform configuration.

Of course, while the above describes a user accessing the tools 145, insome embodiments, a user interface device 112 and/or an accessapplication may access any of the tools 145 in a similar manner.

Each of the tools 150-160 may generate respective outputs 200.Definitions corresponding to the generated outputs 200 may be stored orsaved with the other definitions 162, e.g., either automatically, or inresponse to a user command. In an embodiment, the correspondingdefinitions of the outputs 200 of the tools 150-160 are stored in theprocess control big data storage area 102, for example, as a type oftime-series data 102 a and (optionally) corresponding metadata 102 b.

At least some outputs 200 may be instantiated into the runtimeenvironment of the process control system 10. For example, the modeleditor 152 may generate models 202 (e.g., process control models,network management models, diagnostic models, etc.) or changes to anexisting model 202 that may be downloaded to one or more providerdevices or nodes 110. Corresponding definitions of the generated modelsand/or model changes 202 may be stored in the modeling definitions 168,in an embodiment.

The dashboard editor 150 may generate one or more displays or displaycomponents 205, such as operational, configuration and/or diagnosticdisplays, data analysis displays, and/or graphics or text that may bepresented at user interface devices 112. The dashboard editor 150 mayadditionally generate corresponding bindings 206 for the displays ordisplay components 205 so that they 205 may be instantiated in a runtimeenvironment. In an embodiment, corresponding definitions of thegenerated display/display components 205 and their respective bindings206 may be stored in the display component definitions 165.

The analysis editor 158 may generate data analysis functions,computations, utilities or algorithms 208 (e.g., one or more of the dataanalyses 132 shown in FIG. 4) to be utilized by the process controlsystem big data appliance 102. Corresponding analysis definitions of thegenerated analyses 208 may be stored in the analyses definitions 172,for example.

With particular regard to the data explorer tool 155, the data explorer155 may provide access to historized data stored in the process controlsystem big data storage area 102. The historized data may includetime-series data points 120 a that have been collected during runtime ofthe process control system 10 and have been stored (along with anycorresponding metadata 120 b) in the process control system big datastorage area 120. For example, the historized data may includeindications of models, parameters and parameter values, batch recipes,configurations, etc. that were used during the operation of the processplant 10, and the historized data may include indications of useractions that occurred during the operation of the process plant 10 orrelated activities.

Using the data explorer 155, various visualizations of at least portionsof the stored data 120 may be performed, in an embodiment. For example,the data explorer 155 may utilize one or more data analysis definitions172 to generate and present a data visualization at the data studiointerface or portal 180. Upon viewing the visualization, a user 112 maydiscover a previously unknown data relationship 210. For example, a user112 may discover a data relationship between a particular event, anambient temperature, and a yield of a production line. As such, thediscovered data relationship 210 may be an output 200 of the dataexplorer 155 and may be saved, e.g., as a data definition 170.

In an embodiment, the user 112 may instruct the process control systembig data appliance 102 (e.g., via the analysis editor 158 at the datastudio portal 180) to identify any models 168 that may be affected bythe discovered relationship 210. For example, the user 112 may select,using the analysis editor 158, one or more data analysis engines 132 tooperate on the discovered relationship 210 (and optionally, inconjunction with additional stored data 120). In response to the userinstruction, the process control system big data appliance 102 mayidentify one or more models 168 that are affected by the discovered datarelationship 210. In an embodiment, the process control system big dataappliance 102 may also determine updated parameter values 212 and/or newparameters 215 for the affected models 168 based on the discovered datarelationship 210, and may automatically update the affected models 168accordingly. In an embodiment, the process control system big dataappliance 102 may automatically create a new model 202 based on thediscovered data relationship 210. The process control system big dataappliance 102 may store the updated and/or new models 202, parameters215, parameter values 215, etc. as corresponding definitions 162, in anembodiment. In an embodiment, any of the identified models 168, 202,parameters 215, parameter values 212, etc. may be presented to the user112, e.g., via the portal 180, and instead of automatically implementingchanges, the data appliance 102 may only do so if the user so instructs.

In some embodiments, rather than relying on user directed knowledgediscovery, the process control system big data appliance 102 mayautomatically perform knowledge discovery by automatically analyzinghistorized data. For example, one or more data analysis engines 132 ofthe process control system big data appliance 102 may execute in thebackground to automatically analyze and/or explore one or more runtimestreams of data. For example, the process control system big dataappliance 102 may execute an instance of the data explorer 155 and/orthe analysis editor 158 in the background. Based on the backgroundexploration and analysis, the data analysis engines 132 may discover apreviously unknown data relationship 218. The data analysis engines 132may save the discovered data relationship 218, e.g., in the datadefinitions 170. In an embodiment, the process control system big dataappliance 102 (e.g., the data studio 109, an appliance data receiver 122or other component) may alert or notify a user of the discovered datarelationship 218, e.g., via the portal 180.

In an embodiment, the process control system big data appliance 102 mayautomatically identify stored models, parameters, and/or parametervalues 168 which may be affected by the automatically discovered datarelationship 218, and may determine updated or new parameter values 212,updated or new models 202, and/or other actions to be taken 220 based onthe discovered data relationship 218. The process control system bigdata appliance 102 may suggest the updated/new parameters 215, parametervalues 212, models 202, and/or other actions 220 to a user 112 via theportal 180, in an embodiment. For example, the process control systembig data appliance 102 may suggest new alarm limits, may suggestreplacing a valve, or may suggest a predicted time at which a new areaof the plant 10 is to be installed and operational to optimize output.In an embodiment, the process control system big data appliance 102 mayautomatically apply a new or updated model, parameter, parameter value,or action without informing the user 112.

In an embodiment, the process control big data appliance 102 mayhypothesize candidate models, parameters, parameter values and/oractions to be modified or created, and may test its hypotheses off-line,e.g., against a larger subset of the historized data 120. In thisembodiment, only validated models, parameters, parameter values, and/oractions may be suggested to the user, saved in the definitions 162,and/or automatically applied to the system 10.

Turning now to FIG. 6, FIG. 6 is a block diagram illustrating anembodiment of a coupling between the configuration and explorationenvironment (e.g., the off-line environment) 220 provided by the processcontrol system big data studio 109, and a runtime environment 222 thatis instantiated in the process plant or control system 10. The couplingis effected through one or more scripts 225, in an embodiment.

The scripts 225 may provide one or more capabilities to, for example,download executables 228 corresponding to the definitions 162 of models168, data bindings and dashboard information 165, data relationships170, and/or other aspects from the configuration and explorationenvironment 220 into the runtime environment 222 of one or more nodes108. Accordingly, the scripts 225 may enable on-line access to one ormore components 162 that were developed during an off-line phase (e.g.,by using the tools 145 of the data studio 109). In an embodiment, thescripts 225 may additionally provide capabilities for a node 108 toupload information that is generated or created in the runtimeenvironment 222 to the configuration and exploration environment 220.For example, new or modified models, parameters, analyses or otherentities created at a user interface device 112 may be uploaded andstored as new or modified definitions 162.

In an embodiment, the scripts 225 may download executables 228corresponding to selected definitions 162 to one or more nodes 108. Aparticular download script 225 may be performed in response to a userinstruction, or may be automatically performed by the process controlsystem big data appliance 102. In the runtime environment 222, a runtimeengine 230 (e.g., as executed by a processor such as the processorP_(MCX)) may operate on the executables 228 to instantiate the entitiescorresponding to the selected definitions 162. In an embodiment, eachnode 108 may include a respective runtime engine 230 to operate on thedownloaded executables 228.

With regard to executables 228 corresponding to dashboard displays inparticular, a respective download script 225 may bind data definitions170 and/or model definitions 168 to the dashboard definition 165 togenerate a corresponding dashboard executable 228. In some embodiments,pre-processing may be required to be performed on a dashboard executable228 before loading the corresponding dashboard display 232 andcorresponding data and/or model descriptions in the runtime environment222 at a user interface device 112. In an embodiment, a runtimedashboard support engine 235 may perform the pre-processing and/or theloading in the run-time environment 222. The runtime dashboard supportengine 235 may be, for example, an application in communicativeconnection with the runtime engine 230. The runtime dashboard supportengine 235 may be hosted, for example, at the process control big dataappliance 102, at the user interface device 112, or at least partiallyat the process control big data appliance 102 and at least partially atthe user interface device 112. In some embodiments, the runtime engine230 includes at least a portion of the runtime dashboard support engine235.

FIG. 7 illustrates a flow diagram of an example method 300 forsupporting big data in a process control system or process plant. Themethod 300 may be implemented in the process control system big datanetwork 100 of FIG. 1, or in any other suitable network or system thatsupports big data in a process control system or process plant. In anembodiment, the method 300 is implemented by the process control systembig data appliance 102 of FIG. 1. For illustrative (and non-limiting)purposes, the method 300 is discussed below with simultaneous referenceto FIGS. 1-6.

At a block 302, data may be received. For example, the data may bereceived by the big data appliance 102 of the process control system bigdata network 100, e.g., by one or more data receivers 122. The data maycorrespond to a process plant and/or to a process being controlled by aprocess plant. For example, the data may include real-time datagenerated while controlling a process in the process plant,configuration data, batch data, network management and traffic data ofvarious networks included in the process plant, data indicative of useror operator actions, data corresponding to the operation and status ofequipment and devices included in the plant, data generated by ortransmitted to entities external to the process plant, and other data.

The data may be received from one or more nodes 108 in communicativeconnection with the process control system big data network 100. Forexample, the data may be received from a provider node 110, a userinterface node 112, and/or from another node 115 communicativelyconnected to the process control system big data network 100. Thereceived data may include time series data, for example, where each datapoint is received in conjunction with a time stamp indicating a time ofcollection of the data point at a respective node 108.

In an embodiment, at least a portion of the data may be received using astreaming service. In an embodiment, the streaming service may be hostedby a node 108 of the process control system big data network 100, andthe big data appliance 102 or a data receiver 122 included in the bigdata appliance 102 may subscribe to the streaming service hosted by thenode 108.

At a block 305, the received data may be caused to be stored in aunitary, logical big data storage area such as the process controlsystem big data appliance storage area 120, or some other suitable datastorage area. The unitary, logical big data storage area may store datausing a common format for all types of received data. In particular, thecommon format may enable real-time searching and exploration of thestored data in a timely and efficient manner. In an embodiment, thereceived data is stored in the unitary, logical big data storage area inconjunction with corresponding metadata.

At a block 308, a service may be caused to be performed on at least partof the data stored in the unitary, logical big data storage area. In anembodiment, the big data appliance 102 or an appliance request servicer125 of the big data appliance 102 may cause the service to be performed.The service may be caused to be performed in response to a user request,or the service may be caused to be performed automatically. In anembodiment, the big data appliance 102 may select the service to beperformed.

In an embodiment, the service may be a computational analysis, such as aregression analysis, a cluster analysis, a data trend analysis, or othercomputational analysis. For example, the computational analysis mayoperate on a first subset of data stored in the unitary, logical bigdata storage area, and may generate a result including a second subsetof data. In an embodiment, the result may be presented to a user at auser interface. In an embodiment, the result may be presented to theuser along with one or more suggestions, such as suggestions ofadditional computational analyses that may be desired to be run, aspecific user action to be taken, a time that the specific user actionis suggested to be taken, and the like.

The second subset of data may include a data definition or relationship.For example, the second subset of data may indicate a change to anexisting entity associated with the process control system or plant, ormay indicate a new entity to be associated with the process controlsystem or plant. The changed or new entity may be, for example, adashboard display component, a process model, a function block, a datarelationship, a parameter or parameter value, a binding, or acomputational analysis.

At a block 310, the second set of data may be stored. For example, thesecond set of data may be stored in the unitary, logical big datastorage area.

In some embodiments, at the block 308, a service in addition to orinstead of a computational analysis may be performed. For example, theservice may be a configuration service, a diagnostic service, a controlapplication service, a communication service, an administration service,an equipment management service, a planning service, or some otherservice.

Mobile Control Room

With the process control system big data network 100, dashboardsdisplays 232 and the data studio portal 180 may be available at anyauthenticated user interface device 112. Further, user interface devices112 may be mobile devices. As such, user interfaces and displays thatare provided in prior art control systems only by workstations at fixedcontrol room locations may be available at mobile user interface devices112 in a system 10 supported by the process control system big datanetwork appliance 102. Indeed, in some configurations of a system 10supported by the process control system big data network appliance 102,all user interfaces related to a process plant 10 may be entirelyprovided at a set of mobile user interface devices 112 (e.g., a “mobilecontrol room”), and the process plant 10 may not even include a fixedcontrol room at all. In an embodiment, a user interface device 112 mustbe authenticated in order to perform any and all fixed control roomfunctionality, including configuring and downloading models, creatingand launching applications and utilities, performing activitiespertaining to network management, secured access, system performanceevaluations, product quality control, etc. For example, a user interfacedevice 112 may be authenticated using a procedure such as previouslydiscussed for authenticating devices and nodes at the process controlsystem big data network 100.

To support such a mobile control room, the process control system bigdata network appliance 102 may provide or host one or more mobilecontrol room services. A mobile control room service may be a particulartype of data requester 130, for example, or may be another application.In an embodiment, a web server may be provided at each user interfacedevice 112 to support a web based browser, web based application, orplug-in to interface with the mobile control room services hosted by theappliance 102.

One example of a mobile control room service may include an equipmentawareness service. In this example, as a mobile worker moves his or heruser interface device 112 within the plant 10, various provider devicesor nodes 110 at fixed locations may automatically self-identify to theuser interface device 112, e.g., by using a wireless communicationprotocol such as an IEEE 802.11 compliant wireless local area networkprotocol, a mobile communication protocol such as WiMAX, LTE or otherITU-R compatible protocol, a short-wavelength radio communicationprotocol such as near field communications (NFC) or Bluetooth, a processcontrol wireless protocol such as WirelessHART, or some other suitablewireless communication protocol. The user interface device 112 and afixed provider device 110 may automatically authenticate and form asecure, encrypted connection (e.g., in a manner such as previouslydiscussed for the user interface device 112 and the data studio 109). Inan embodiment, the equipment awareness service may cause one or moreapplications that specifically pertain to the fixed provider device 110to be automatically launched at the user interface device 112, such as awork order, a diagnostic, an analysis, or other application.

Another example mobile control room service may be a location and/orscheduling awareness service. In this example, the location and/orscheduling awareness service may track a mobile worker's location,schedule, skill set, and/or work item progress, e.g., based on themobile worker's authenticated user interface device 112. Based on thetracking, the location and/or scheduling awareness service at theappliance 102 may enable plant maps, equipment photos or videos, GPScoordinates and other information corresponding to a worker's locationto be automatically determined and displayed on the user interfacedevice 112 to aid the mobile worker in navigation and equipmentidentification. Additionally or alternatively, as a mobile worker mayhave a particular skill set, the location and/or scheduling awarenessservice may automatically customize the appearance of the worker'sdashboard 232 based on the skill sets and/or the location of the userinterface device 112. In another scenario, the location and/orscheduling awareness service may inform the mobile worker in real-timeof a newly opened work item that pertains to a piece of equipment in hisor her vicinity and that the mobile worker is qualified to address. Inyet another scenario, the location and/or scheduling awareness servicemay cause one or more applications that specifically pertain to thelocation and/or skill set of the mobile worker to be automaticallylaunched at the user interface device 112.

Still another example mobile control room service may be a mobile workercollaboration service. The mobile worker collaboration service may allowa secure collaboration session to be established between at least twouser interface devices 112. In an embodiment, the secure collaborationsession may be automatically established when the two devices 112 moveinto each other's proximity and become mutually aware of one another,e.g., by using a wireless protocol such as discussed above with respectto the equipment awareness service. Once the session is established,synchronization of data between the user interface devices 112 during acollaborative work session may be performed.

Yet another example mobile control room service may be a mobile workerapplication synchronization service. This service may allow a mobileworker to move his or her work between different hardware platforms(e.g., a mobile device, a work station, a home computing device, atablet, and the like) while maintaining the state of his or her work invarious applications. In embodiment, application synchronization may beautomatically performed when two different hardware platform devices 112move into each other's proximity and become mutually aware of oneanother, e.g., via a wireless protocol such as discussed above withrespect to the equipment awareness service. For example, a mobile workermay simply bring his or her tablet into the vicinity of an officedesktop computer to seamlessly continue work that was started in thefield.

Of course, other mobile control room services in addition to the onesdiscussed herein may be possible, and may be supported by the processcontrol system big data appliance network 100.

Embodiments of the techniques described in the present disclosure mayinclude any number of the following aspects, either alone orcombination:

1. A system for supporting big data in a process control plant,comprising a unitary, logical data storage area including one or moredata storage devices configured to store, using a common format, datacorresponding to at least one of the process plant or a process that iscontrolled in the process plant, the data including multiple types ofdata, and a set of types of data including configuration data,continuous data, and event data corresponding to the process. The systemmay further comprise one or more data receiver computing devicesconfigured to receive the data from one or more other devices and tocause the received data to be stored in the unitary, logical datastorage area.

2. The system of the previous aspect, wherein the data includes timeseries data.

3. The system of any of the preceding aspects, wherein a data entry ofthe time series data stored in the unitary, logical data storage areaincludes a content and a timestamp, the timestamp being indicative of atime of generation of the content of the data entry.

4. The system of any of the preceding aspects, wherein the unitary,logical data storage area is further configured to store metadatacorresponding to the data.

5. The system of any of the preceding aspects, wherein the data isstored using a common structured format, and wherein the metadata isstored using an unstructured format.

6. The system of any of the preceding aspects, wherein the data furtherincludes at least one of: data indicative of a health of a machineincluded in the process plant, data indicative of a health of aparticular piece of equipment included in the process plant, dataindicative of a health of a particular device included in the processplant, or data corresponding to a parameter related to safety of theprocess plant.

7. The system of any of the preceding aspects, wherein the data furtherincludes at least one of: data describing a user input entered at one ofthe one or more other devices; data describing a communication networkof the process plant; data received from a computing system external tothe process plant; or data received from another process plant.

8. The system of any of the preceding aspects, wherein the datadescribing the communication network of the process plant comprises datadescribing at least one of a performance, a resource, or a configurationof the communication network.

9. The system of any of the preceding aspects, wherein the one or moredata storage devices are included in at least one of: a data bank, aRAID storage system, a cloud data storage system, a distributed filesystem, or other mass data storage system.

10. The system of any of the preceding aspects, wherein at least theportion of the data is streamed using a streaming service hosted by atleast one of the one or more other devices, and wherein the unitary,logical data storage area or at least one of the one or more datareceiver computing devices is a subscriber to the streaming service.

11. The system of any of the preceding aspects, wherein the one or moreother devices includes: a field device and a controller that arecommunicatively coupled to control a process in the process plant, andat least one of a user interface device or a network management device.

12. The system of any of the preceding aspects, wherein all datagenerated at and received by at least one of the one or more otherdevices is caused to be stored at the unitary, logical data storagearea.

13. The system of any of the preceding aspects, wherein the systemfurther comprises a set of request servicer computing devices configuredto perform one or more services using at least a portion of the datastored in the unitary, logical data storage area, the one or moreservices including a computational analysis.

14. The system of any of the preceding aspects, wherein at least onedata receiver computing device and at least one request servicercomputing device are an integral computing device.

15. The system of any of the preceding aspects, wherein at least one ofthe request servicer computing devices is further configured todetermine, based on an execution of the computational analysis, a changeto a configured entity included in the process plant.

16. The system of any of the preceding aspects, wherein the at least oneof the request servicer computing devices is further configured to atleast one of: (i) present the determined change at a user interface, or(ii) automatically apply the change to the configured entity.

17. The system of any of the preceding aspects, wherein the one or moreservices further include a service to generate a set of definitionscorresponding to a set of entities that are able to be instantiated in aruntime environment of the process plant.

18. The system of any of the preceding aspects, wherein the set ofentities includes at least one of: a configurable device, a diagnosticapplication, a display view application, a control model, or a controlapplication.

19. The system of any of the preceding aspects, wherein the set ofdefinitions is generated in an offline environment of the process plant,and wherein the system further comprises a set of scripts to transformat least one definition included in the set of definitions, and to loadthe transformed at least one definition into the runtime environment ofthe process plant.

20. The system of any of the preceding aspects, wherein the at least onedefinition is generated in the offline environment in response to a userinput.

21. The system of any of the preceding aspects, wherein the at least onedefinition is generated in the offline environment automatically.

22. The system of any of the preceding aspects, wherein at least one ofthe one or more services is a web service.

23. A method for supporting big data in a process control plant,executed by any of the systems of any of the aspects described herein.The method may include receiving, at one or more data receiver computingdevices, data corresponding to at least one of the process control plantor a process controlled by the process control plant; and causing thereceived data to be stored, using a common format, in a unitary, logicaldata storage area, the unitary, logical data storage area including oneor more data storage devices configured to store multiple types of datausing a common format, and a set of types of data includingconfiguration data, continuous data, and event data corresponding to theprocess.

24. The method of the preceding aspect, wherein receiving the datacomprises receiving at least a portion of the data using a streamingservice.

25. The method of any of the preceding aspects, further comprisingsubscribing to the streaming service.

26. The method of any of the preceding aspects, wherein receiving thedata comprises receiving the data from one or more other devicesincluded in the process plant, the one or more devices including acontroller in communicative connection with a field device to controlthe process.

27. The method of any of the preceding aspects, further comprisingcausing a service to be performed using at least a portion of the datastored in the unitary, logical data storage area.

28. The method of any of the preceding aspects, wherein causing theservice to be performed comprises causing a computational analysis to beperformed.

29. The method of any of the preceding aspects, wherein causing thecomputational analysis to be performed comprises causing thecomputational analysis to be performed in response to a user request.

30. The method of any of the preceding aspects, wherein causing thecomputational analysis to be performed comprises causing thecomputational analysis to be selected and performed automatically by thesystem.

31. The method of any of the preceding aspects, wherein the at least aportion of the data stored in the unitary, logical data storage area isa first set of data, and the method further comprises generating asecond set of data based on an execution of the computational analysison the first set of data.

32. The method of any of the preceding aspects, further comprisingstoring the second set of data in the unitary, logical data storagearea.

33. The method of any of the preceding aspects, wherein storing thesecond set of data comprises storing at least one of: a displaycomponent definition, a binding definition, a process model definition,a data definition, a data relationship, or a definition of anothercomputational analysis.

34. One or more tangible, non-transitory computer-readable storage mediastoring computer-executable instructions thereon that, when executed bya processor, perform the method of any of the preceding aspects.

35. A system, comprising any number of the preceding aspects. The systemmay be a process control system, and may further include: a controllerconfigured to control a process in the process control system; a fielddevice communicatively connected to the controller, the field deviceconfigured to perform a physical function to control the process in theprocess control system, and the field device configured to transmit toor receive from the controller real-time data corresponding to thephysical function; and a process control system big data apparatus. Theprocess control system big data apparatus may include: a unitary,logical data storage area including one or more data storage devicesconfigured to store, using a common format, configuration datacorresponding to the controller and the real-time data; and one or moredata receiver computing devices to receive the real-time data and tocause the received data to be stored in the unitary, logical datastorage area. The controller may be a first node of a process controlsystem big data network, and the process control system big dataapparatus may be a second node of the process control system big datanetwork.

36. The system of any of the preceding aspects, wherein the processcontrol system big data network includes at least one of a wiredcommunication network or a wireless communication network.

37. The system of any of the preceding aspects, wherein the processcontrol system big data network is at least partially an ad-hoc network.

38. The system of any of the preceding aspects, wherein the processcontrol system big data network is a first communication network, andwherein the field device is communicatively connected to the controllervia a second communication network different than the firstcommunication network.

39. The system of any of the preceding aspects, wherein the processcontrol system big data network further includes one or more othernodes, the one or more other nodes including at least one of: a userinterface device, a gateway device, an access point, a routing device, anetwork management device, or an input/output (I/O) card coupled to thecontroller or to another controller.

40. The system of any of the preceding aspects, wherein the controlleris configured to cache the real-time data, and wherein an indication ofan identity of the real-time data is excluded from a configuration ofthe controller.

41. The system of any of the preceding aspects, further comprising aprocess control system big data user interface configured to enable auser, via a user interface device, to perform at least one user actionfrom a set of user actions including: view at least a portion of thedata stored in the unitary, logical big data storage area; request aservice to be performed, the service requiring the at least the portionof the data stored in the unitary, logical big data storage area; view aresult of a performance of the service; configure an entity included inthe process control system; cause the entity to be instantiated in theprocess control system; and configure an additional service. The userinterface device may be a third node of the process control system bigdata network.

42. The system of any of the preceding aspects, wherein the processcontrol system big data user interface is configured to authenticate atleast one of the user or the user interface device, and wherein one ormore user actions included in the set of user actions is made availableto the user for selection based on the authentication.

When implemented in software, any of the applications, services, andengines described herein may be stored in any tangible, non-transitorycomputer readable memory such as on a magnetic disk, a laser disk, solidstate memory device, molecular memory storage device, or other storagemedium, in a RAM or ROM of a computer or processor, etc. Although theexample systems disclosed herein are disclosed as including, among othercomponents, software and/or firmware executed on hardware, it should benoted that such systems are merely illustrative and should not beconsidered as limiting. For example, it is contemplated that any or allof these hardware, software, and firmware components could be embodiedexclusively in hardware, exclusively in software, or in any combinationof hardware and software. Accordingly, while the example systemsdescribed herein are described as being implemented in software executedon a processor of one or more computer devices, persons of ordinaryskill in the art will readily appreciate that the examples provided arenot the only way to implement such systems.

Thus, while the present invention has been described with reference tospecific examples, which are intended to be illustrative only and not tobe limiting of the invention, it will be apparent to those of ordinaryskill in the art that changes, additions or deletions may be made to thedisclosed embodiments without departing from the spirit and scope of theinvention.

What is claimed:
 1. A system for supporting big data in a processcontrol plant, comprising: a unitary, logical data storage areaincluding one or more data storage devices configured to store, using acommon format, data corresponding to at least one of the process plantor a process that is controlled in the process plant, the data includingmultiple types of data, and a set of types of data includingconfiguration data, continuous data, and event data corresponding to theprocess; and one or more data receiver computing devices configured toreceive the data from one or more other devices via a process controlbig data network and to cause the received data to be stored in theunitary, logical data storage area, wherein at least one of the one ormore other devices communicates with one or more field devices in theprocess plant via another communication network different from theprocess control big data network, each of the one or more other devicesbeing a respective node of the process control big data network that (i)collects data that is generated by the respective node, (ii) transmitsthe collected data via the process control big data network to the oneor more data receiver computing devices, and (iii) excludes a respectiveconfiguration or definition indicating a rate at which the collecteddata is to be transmitted by the respective node.
 2. The system of claim1, wherein the data stored in the unitary, logical data storage areaincludes time series data, and wherein a data entry of the time seriesdata stored in the unitary, logical data storage area includes contentof a respective data point of the time series data and a timestamp, thetimestamp being indicative of a time of generation of the content of thedata point corresponding to the data entry.
 3. The system of claim 1,wherein the unitary, logical data storage area is further configured tostore metadata corresponding to the received data, the received data isstored using a common structured format, and the metadata is storedusing an unstructured format.
 4. The system of claim 1, wherein the datastored in the unitary, logical data storage area further includes atleast one of: data indicative of a health of a machine included in theprocess plant, data indicative of a health of a particular piece ofequipment included in the process plant, data indicative of a health ofa particular device included in the process plant, data corresponding toa parameter related to safety of the process plant, or datacorresponding to at least one of a performance, a resource, or aconfiguration of the another communication network.
 5. The system ofclaim 1, wherein the received data includes data received from anotherprocess plant.
 6. The system of claim 1, wherein the one or more datastorage devices are included in a cloud data storage system.
 7. Thesystem of claim 1, wherein at least the portion of the received data isstreamed over the process control big data network using a streamingservice hosted by at least one of the one or more other devices, andwherein the unitary, logical data storage area or at least one of theone or more data receiver computing devices is a subscriber to thestreaming service.
 8. The system of claim 1, wherein the one or moreother devices includes: a controller that communicates with at least oneof the one or more field devices via the another communication networkto control the process.
 9. The system of claim 8, wherein the one ormore other devices further includes at least one of a user interfacedevice or a network management device.
 10. The system of claim 1,wherein the system further comprises a set of request servicer computingdevices configured to perform one or more services using at least aportion of the data stored in the unitary, logical data storage area,the one or more services including a computational analysis.
 11. Thesystem of claim 10, wherein at least one of the request servicercomputing devices is further configured to determine, based on anexecution of the computational analysis, a change to a configured entityincluded in the process plant and at least one of: (i) present thedetermined change at a user interface, or (ii) automatically apply thechange to the configured entity.
 12. The system of claim 10, wherein theone or more services further include a service to generate a set ofdefinitions corresponding to a set of entities that are able to beinstantiated in a runtime environment of the process plant, wherein theset of entities includes at least one of: a configurable device, adiagnostic application, a display view application, a control model, ora control application, and wherein the set of definitions are able to betransformed and loaded into the runtime environment of the processplant.
 13. A method for supporting big data in a process control plant,comprising: receiving, via a process control big data network at one ormore data receiver computing devices from each of one or more nodes ofthe process control big data network, data corresponding to at least oneof the process plant or a process controlled by the process plant,wherein at least one of the one or more nodes communicates with one ormore field devices in the process plant via another communicationnetwork different from the process control big data network, the datareceived from the each node including data that is generated by the eachnode while the process is being controlled, and the each node excludinga respective configuration or definition indicating a rate at which datais to be transmitted by the each node via the process control big datanetwork; and causing the received data to be stored, using a commonformat, in a unitary, logical data storage area, the unitary, logicaldata storage area including one or more data storage devices configuredto store multiple types of data using a common format, and a set oftypes of data including configuration data, continuous data, and eventdata corresponding to the process.
 14. The method of claim 13, whereinthe one or more nodes of the process control big data network includesone or more other devices included in the process plant, and the one ormore other devices includes a controller in communicative connection,via the another communication network, with at least one of the one ormore field devices to control the process.
 15. The method of claim 13,further comprising causing a computational analysis to be automaticallyperformed using at least a portion of the data stored in the unitary,logical data storage area.
 16. The method of claim 15, wherein the atleast a portion of the data stored in the unitary, logical data storagearea is a first set of data; and the method further comprises:generating a second set of data based on an execution of thecomputational analysis on the first set of data, and storing the secondset of data in the unitary, logical data storage area.
 17. The method ofclaim 16, wherein the second set of data includes at least one of: adisplay component definition, a binding definition, a process modeldefinition, a data definition, a data relationship, or a definition ofanother computational analysis.
 18. The method claim 16, wherein thecomputational analysis is a first service; and the method furthercomprises causing a second service to be automatically performed usingthe second set of data.
 19. A process control system, comprising: acontroller configured to control a process in the process controlsystem; a field device communicatively connected to the controller via acommunication network of the process plant, the field device configuredto perform a physical function to control the process in the processcontrol system, and the field device configured to transmit to orreceive from the controller, via the communication network, real-timedata corresponding to the physical function; and a process controlsystem big data apparatus, the process control system big data apparatusincluding: a unitary, logical data storage area including one or moredata storage devices configured to store, using a common format, thereal-time data and configuration data corresponding to the controller;and one or more data receiver computing devices to (i) receive, via aprocess control system big data network different than the communicationnetwork, the real-time data and the configuration data corresponding tothe controller, and (ii) to cause the received data to be stored in theunitary, logical data storage area; wherein: the controller is a firstnode of a process control system big data network, the process controlsystem big data apparatus is a second node of the process control systembig data network, and at least one of the field device or the controllerexcludes a respective configuration or definition indicating a rate atwhich the real-time data corresponding to the physical function is to betransmitted by the at least one of the field device or the controllervia the process control system big data network.
 20. The process controlsystem of claim 19, wherein the process control system big data networkfurther includes one or more other nodes, the one or more other nodesincluding at least one of: a user interface device, a gateway device, anaccess point, a routing device, a network management device, or aninput/output (I/O) card coupled to the controller or to anothercontroller.
 21. The process control system of claim 19, furthercomprising a process control system big data user interface configuredto enable a user, via a user interface device, to perform at least oneuser action from a set of user actions including: view at least aportion of the data stored in the unitary, logical data storage area;request a service to be performed, the service requiring at least theportion of the data stored in the unitary, logical data storage area;view a result of a performance of the service; or configure anadditional service.
 22. The process control system of claim 21, wherein:the process control system big data user interface is configured toauthenticate at least one of the user, the user interface device, or anaccess application running on the user interface device; and one or moreuser actions included in the set of user actions is made available tothe user for selection based on the authentication.